Difference between revisions of "Challenges in Engineering Self-Organizing Systems"

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* '''Design of emergence''':
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** How to design ''local rules'' achieving the desired ''global properties''?
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** How to couple local (unit) behaviors to global (system) behavior?
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** Non-trivial but approaches exist (eg. evolutionary design), Bio-inspired techniques  (stigmergy, social insects metaphors, immune systems, evolutionary algorithms), these approaches perform well in some specific domains (dynamic networks : routing, P2P, ..)
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** Definition of fitness function (designer needs to provide intermediate goals first (eg. subgoals like ball handling in a soccer simulation), but overall system can later revise/remove those intermediate steps (only winning the game counts))
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* '''Design of the representation / communication / interaction''':
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** Degrees of freedom and adaptability
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** How to represent/set up the problem (cartesian vs. polar spatial coordinates in the sensor protocol made a difference in evolving a control system: [http://wwwu.uni-klu.ac.at/welmenre/papers/fehervari-2010-Evolving_Neural_Network_Controllers_for_a_Team_of_Self-organizing_Robots.pdf I. Fehervari and W. Elmenreich. '''Evolving neural network controllers for a team of self-organizing robots'''. Journal of Robotics, 2010.])
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** The representation of a problem is very important (see [http://pespmc1.vub.ac.be/papers/Problem-Formulation.html Heylighen F. (1988): '''Formulating the Problem of Problem-Formulation''', in: Cybernetics and Systems '88, Trappl R. (ed.), (Kluwer Academic Publishers, Dordrecht), p. 949-957.])
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* '''Simple versus chaotic behavior''': Can we describe the system state?
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** The state of some self-organizing systems can be easily modeled (firefly sync)
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** The state of other self-organizing systems cannot be modeled, they exhibit chaotic behavior, which makes it impossible to predict future states.
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* '''Robustness issues'''
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** Malicious nodes, faults, defects
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* '''Testing''':
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** It can be very difficult to test a proposed self-organizing system with respect to a given goal (many entities, large operational range, chaotic behavior)
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** Rare events may lead to major global effects.
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** Repeatability of results
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** even simple deterministic systems can bear an interesting, unexpected (unwanted?) behavior, cf. [http://en.wikipedia.org/wiki/Langton%27s_ant '''Langton's Ant'''].
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* '''User aspects'''
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** To what extend can today’s systems be replaced or complemented by self-organizing systems, taking into account
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*** constraints and acceptance of the technology and
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*** risks for users?
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* '''Reliability and trust''' (this point is somehow connected to the aboce two points on testing and user aspects)
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** ''"Hey, look, I have just evolved a complex control algorithm for the cooling system of the reactor."''
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* '''(Unified?/complete) Theory of Self-organizing systems'''
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** Standards of System Theory? :  General Control Theory
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** Deterministic, non linear dynamic systems (Chaos Theory)
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** Cellular Automata?
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** Conceptual/philosophical : Autopeïsis  & Enaction Theory (Maturana & Varela) ⇒ Structural/ Behavioural Coupling
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** Complexity Theory and Complex Networks Theory
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==Documents from group work==
 
==Documents from group work==
 
* [[Group 2]]
 
* [[Group 2]]

Latest revision as of 09:14, 14 July 2010

  • Design of emergence:
    • How to design local rules achieving the desired global properties?
    • How to couple local (unit) behaviors to global (system) behavior?
    • Non-trivial but approaches exist (eg. evolutionary design), Bio-inspired techniques (stigmergy, social insects metaphors, immune systems, evolutionary algorithms), these approaches perform well in some specific domains (dynamic networks : routing, P2P, ..)
    • Definition of fitness function (designer needs to provide intermediate goals first (eg. subgoals like ball handling in a soccer simulation), but overall system can later revise/remove those intermediate steps (only winning the game counts))
  • Simple versus chaotic behavior: Can we describe the system state?
    • The state of some self-organizing systems can be easily modeled (firefly sync)
    • The state of other self-organizing systems cannot be modeled, they exhibit chaotic behavior, which makes it impossible to predict future states.
  • Robustness issues
    • Malicious nodes, faults, defects
  • Testing:
    • It can be very difficult to test a proposed self-organizing system with respect to a given goal (many entities, large operational range, chaotic behavior)
    • Rare events may lead to major global effects.
    • Repeatability of results
    • even simple deterministic systems can bear an interesting, unexpected (unwanted?) behavior, cf. Langton's Ant.
  • User aspects
    • To what extend can today’s systems be replaced or complemented by self-organizing systems, taking into account
      • constraints and acceptance of the technology and
      • risks for users?
  • Reliability and trust (this point is somehow connected to the aboce two points on testing and user aspects)
    • "Hey, look, I have just evolved a complex control algorithm for the cooling system of the reactor."
  • (Unified?/complete) Theory of Self-organizing systems
    • Standards of System Theory? : General Control Theory
    • Deterministic, non linear dynamic systems (Chaos Theory)
    • Cellular Automata?
    • Conceptual/philosophical : Autopeïsis & Enaction Theory (Maturana & Varela) ⇒ Structural/ Behavioural Coupling
    • Complexity Theory and Complex Networks Theory


Documents from group work

Examples of self-organizing behavior in technology

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